Project Abstract Although Wedge Resection Surgery results in better lung function, tumor recurrence rate is almost double that of lobectomy, with significantly poorer 5-year survival rates. This may be attributed to the difficulty in accurately localizing and resecting the nodules in a deflated lung. Currently, there is no effective method of accurately localizing the nodules and guiding the surgical stapling device to the optimal resection margin. The long-term goal of this research is to investigate algorithms and technologies to manage lung nodules from diagnosis to surgical resection. The objective of this proposal is to design and develop a lung navigation (LungNav) system to localize and excise, with sufficient margin, small and early malignant lung nodules. The experimental methods will be to design and develop the LungNav system integrated with an active nodule tracker called J-bar, machine learning algorithms for determining the optimal resection margin, and tracked surgical stapling device for accurately excising the nodule. Tumor deformation algorithms and augmented reality displays will be developed to visualize the nodule on thoracosopy videos and guide the surgical stapler in real-time to the optimal margin. The hypothesis is that by anchoring the J-bar close to the nodule, the nodule position can be accurately tracked in real-time despite significant tissue deformation when the lung is collapsed and manipulated during surgery. To achieve the goals of this project, we will pursue the following specific aims: 1) Design and develop the nodule tracker (J-bar) and deformation algorithms to estimate the real-time position of the nodule. 2) Investigate a machine-learning approach based on convolutional neural networks (CNN) to determine the optimal resection margin. 3) Design and develop a software navigation module, called LungNav, for visualizing the tumor and navigating the surgical stapler to the optimal resection margin. 4) Validate the design and performance of the LungNav system using ex-vivo lung tissue and live porcine models. The proposed research is significant since it addresses an important problem, which potentially affects several thousand patients each year, of accurately localizing and resecting lung nodules while preserving healthy lung function. The research is innovative since it builds on state-of-the-art machine learning algorithms, navigation systems and augmented reality methods to accurately diagnose and localize the nodule in presence of significant tissue deformation. The expected outcome of the project is the development of CNN-based machine learning algorithms for lung nodule classification and a LungNav system with tumor deformation algorithms and augmented reality methods to localize and guide complete surgical resection of lung nodules.